RNN_many2one_StackedLSTM.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNVCq4dNxank3c4cP47BF9G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DaJaCruz/PhD/blob/main/RNN_many2one_StackedLSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import packages\n",
        "\n",
        "import tensorflow as tf\n",
        "import pandas as pd # Pandas dataframe (very common in ML)\n",
        "import numpy as np # import numpy to handle a lot of things!\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM#, CuDNNLSTM\n",
        "import matplotlib.pyplot as plt # import plotting tools to create figures\n",
        "from IPython.display import display, Math # to print with Latex math\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from matplotlib import cm # to change colors of surface plots\n",
        "\n",
        "# Uncomment line below if you want to enable live rotation of the surface plots.\n",
        "#%matplotlib notebook\n"
      ],
      "metadata": {
        "id": "M9EgUFg2IHFx"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! sudo apt-get install texlive-latex-recommended \n",
        "! sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended  \n",
        "! wget http://mirrors.ctan.org/macros/latex/contrib/type1cm.zip \n",
        "! unzip type1cm.zip -d /tmp/type1cm \n",
        "! cd /tmp/type1cm/type1cm/ && sudo latex type1cm.ins\n",
        "! sudo mkdir /usr/share/texmf/tex/latex/type1cm \n",
        "! sudo cp /tmp/type1cm/type1cm/type1cm.sty /usr/share/texmf/tex/latex/type1cm \n",
        "! sudo texhash \n",
        "!apt install cm-super"
      ],
      "metadata": {
        "id": "18JkJKGVYYc4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic plotting tools needed in Python.\n",
        "\n",
        "#%config InlineBackend.figure_format = \"retina\" # render higher resolution images in the notebook\n",
        "#plt.style.use(\"seaborn\") # style for plotting that comes from seaborn\n",
        "FS = 26  # Font Size\n",
        "plt.rcParams['axes.facecolor'] = (0.97, 0.97, 0.97)\n",
        "plt.rcParams['figure.facecolor'] = (1, 1, 1)\n",
        "plt.rcParams[\"font.family\"] = \"sans\"\n",
        "plt.rcParams[\"font.serif\"] = \"Times New Roman\"\n",
        "plt.rcParams['font.size'] = FS\n",
        "params = {\"ytick.color\": (0, 0, 0),\n",
        "          \"xtick.color\": (0, 0, 0),\n",
        "          \"grid.color\": (.9, .9, .9),\n",
        "          \"text.color\": (0, 0, 0),\n",
        "          \"axes.labelcolor\": (0, 0, 0),\n",
        "          \"axes.edgecolor\": (.15, .15, .15)#,\n",
        "          #\"text.usetex\": True\n",
        "          }\n",
        "plt.rcParams.update(params)\n",
        "linewidth=3.0"
      ],
      "metadata": {
        "id": "o4XwEKJAsAEC"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import raw data\n",
        "url = 'https://raw.githubusercontent.com/DaJaCruz/PhD/main/data91_all.csv'\n",
        "df = pd.read_csv(url, delimiter=';')"
      ],
      "metadata": {
        "id": "d1sGiD1LmdJ_"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Split raw data in Input (X) and Output (Y) data\n",
        "X_data_raw = df.loc[:,['Input_4','Input_5']].values\n",
        "Y_data_raw = df.loc[:,['Output_1','Output_3']].values"
      ],
      "metadata": {
        "id": "s_r3SHYvIInJ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_, idx = np.unique(Y_data_raw, axis=0, return_index=True)\n",
        "Y_data_unique_sorted = Y_data_raw[np.sort(idx)]\n",
        "Y_dat_unique = Y_data_unique_sorted[:, 0].reshape(-1, 1)\n",
        "print(X_data_raw.shape, Y_data_unique_sorted.shape)\n"
      ],
      "metadata": {
        "id": "1vsbaLA7_rWZ",
        "outputId": "a61786e1-dc1b-44aa-ecfb-7f34187fd6ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(45500, 2) (91, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape data \n",
        "X_data_shaped = X_data_raw.reshape((91, 500, 2))\n",
        "Y_data_shaped = Y_dat_unique.reshape((91, 1, 1))"
      ],
      "metadata": {
        "id": "0TPGNJ1p4ohv"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Select the number of cases used \n",
        "n_cases = 20\n",
        "total_n_points= n_cases*500\n",
        "X_data = X_data_shaped[:n_cases, :, :]\n",
        "Y_data = Y_data_shaped[:n_cases, :, :]"
      ],
      "metadata": {
        "id": "0uoIyGIYK2lR"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape X_data and Y_data to Normalization operation\n",
        "X_data_reshaped_norm=X_data.reshape(total_n_points, 2)\n",
        "Y_data_reshaped_norm=Y_data.reshape(n_cases, 1)"
      ],
      "metadata": {
        "id": "SebYiWHyFPn2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "#Scale Inputs with a [-1,1] scaler\n",
        "scaler_x = MinMaxScaler(feature_range=(-1,1))\n",
        "#\n",
        "# Scaling inputs with a Standard Scaler:\n",
        "#scaler_x = StandardScaler()\n",
        "#\n",
        "scaler_x.fit(X_data_reshaped_norm)# fit the scaler to the input data\n",
        "#\n",
        "X_data_scaled_shaped=scaler_x.transform(X_data_reshaped_norm) # scale the input data (the whole dataset)\n",
        "#\n",
        "#Scale Inputs with a [-1,1] scaler\n",
        "scaler_y = MinMaxScaler(feature_range=(-1,1))\n",
        "#\n",
        "# Scaling inputs with a Standard Scaler:\n",
        "#scaler_y = StandardScaler()\n",
        "#\n",
        "scaler_y.fit(Y_data_reshaped_norm) # fit the scaler to the output data\n",
        "Y_data_scaled_shaped=scaler_y.transform(Y_data_reshaped_norm) # scale the output data (the whole dataset)"
      ],
      "metadata": {
        "id": "kLHpLfs94Iuo"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape data \n",
        "X_data_scaled = X_data_scaled_shaped.reshape((n_cases, 500, 2))\n",
        "Y_data_scaled = Y_data_scaled_shaped.reshape((n_cases, 1, 1))"
      ],
      "metadata": {
        "id": "dEJcB2RtRP4h"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('X_data =', X_data.shape)\n",
        "print('Y_data =', Y_data.shape)\n",
        "print('X_data_scaled =', X_data_scaled.shape)\n",
        "print('Y_data_Scaled =', Y_data_scaled.shape)"
      ],
      "metadata": {
        "id": "C4iYXPjEDHgZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plots with normalized and Non-normalized inputs\n",
        "fig_ANN, ((ax1_ANN, ax2_ANN), (ax3_ANN, ax4_ANN)) = plt.subplots(nrows=2, ncols=2, figsize=(30, 20))\n",
        "\n",
        "#Case Studies to represent\n",
        "Case_1 = 3\n",
        "Case_2 = 4\n",
        "Case_3 = 5\n",
        "\n",
        "#Force Displacement Curves\n",
        "ax1_ANN.plot(X_data[Case_1,:,0], X_data[Case_1,:,1], 'o', markersize=10, color='blue', label=\"training points\") # show Non-normalized training data\n",
        "ax1_ANN.plot(X_data[Case_2,:,0], X_data[Case_2,:,1], 'o', markersize=10, color='gray', label=\"training points\") # show Non-normalized training data\n",
        "ax1_ANN.plot(X_data[Case_3,:,0], X_data[Case_3,:,1], 'o', markersize=10, color='red', label=\"training points\") # show Non-normalized training data\n",
        "ax1_ANN.set_ylabel('Punch Force, $F_p$ [kN]')\n",
        "ax1_ANN.set_xlabel('Time, $t$ [s]')\n",
        "ax1_ANN.set_title('Non-normalized input data')\n",
        "#ax1_ANN.legen#d(['training', 'testing', 'validation' ], loc='best')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "# ax1_ANN.set_ylim([0, 10])\n",
        "# ax1_ANN.set_xlim([0, 20])\n",
        "\n",
        "# K-n Points\n",
        "ax2_ANN.plot(Case_1, Y_data[Case_1,0,:], 'o', markersize=10, color='blue') # show normalized training data\n",
        "ax2_ANN.plot(Case_2, Y_data[Case_2,0,:], 'o', markersize=10, color='gray') # show normalized training data\n",
        "ax2_ANN.plot(Case_3, Y_data[Case_3,0,:], 'o', markersize=10, color='red') # show normalized training data\n",
        "ax2_ANN.set_ylabel('$K$')\n",
        "ax2_ANN.set_xlabel('$n$')\n",
        "ax2_ANN.set_title('Non-normalized output data')\n",
        "# ax2_ANN.legend(['training', 'testing', 'validation'], loc='upper right')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "# ax2_ANN.set_ylim([200, 1800])\n",
        "# ax2_ANN.set_xlim([0, 0.4])\n",
        "\n",
        "ax3_ANN.plot(X_data_scaled[Case_1,:,0], X_data_scaled[Case_1,:,1], 'o', markersize=10, color='blue', label=\"training points\") # show Non-normalized training data\n",
        "ax3_ANN.plot(X_data_scaled[Case_2,:,0], X_data_scaled[Case_2,:,1], 'o', markersize=10, color='gray', label=\"training points\") # show Non-normalized training data\n",
        "ax3_ANN.plot(X_data_scaled[Case_3,:,0], X_data_scaled[Case_3,:,1], 'o', markersize=10, color='red', label=\"training points\") # show Non-normalized training data\n",
        "ax3_ANN.set_ylabel('Punch Force, $F_p$ [kN]')\n",
        "ax3_ANN.set_xlabel('Time, $t$ [s]')\n",
        "ax3_ANN.set_title('Normalized input data')\n",
        "#ax1_ANN.legen#d(['training', 'testing', 'validation' ], loc='best')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "# ax3_ANN.set_ylim([-1, 1])\n",
        "# ax3_ANN.set_xlim([-1, 1])\n",
        "\n",
        "# K-n Points\n",
        "ax4_ANN.plot(Case_1, Y_data_scaled[Case_1,0,:], 'o', markersize=10, color='blue') # show normalized training data\n",
        "ax4_ANN.plot(Case_3, Y_data_scaled[Case_2,0,:], 'o', markersize=10, color='gray') # show normalized training data\n",
        "ax4_ANN.plot(Case_2, Y_data_scaled[Case_3,0,:], 'o', markersize=10, color='red') # show normalized training data\n",
        "ax4_ANN.set_ylabel('$K$')\n",
        "ax4_ANN.set_xlabel('$n$')\n",
        "ax4_ANN.set_title('Normalized output data')\n",
        "# ax2_ANN.legend(['training', 'testing', 'validation'], loc='upper right')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "# ax4_ANN.set_ylim([-1.25, 1.25])\n",
        "# ax4_ANN.set_xlim([-1.25, 1.25])\n",
        "\n",
        "\n",
        "# Create figure with specified size\n",
        "# fig_ANN.set_size_inches(22, 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "vg4v4N2Nq75W",
        "outputId": "c0424835-b473-4bd0-b704-e36043f3872b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Normalized output data')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x2000 with 4 Axes>"
            ],
            "image/png": 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HH39sKisrA20rKirMokWL6pSjGDVqVJOPddOmTUaSycrKMn/4wx/MZ599Zvbv31/vsd9++6259957TVJSUtA4l19+eZPj1PecROuq16KiIpObmxv27brrrqvzfDf0Ht27d68ZNGhQ0LFdu3Y1v/vd7+q9gvOHH34w99xzT52r1UPJpnb55ZfXmVd2drb54osvgo4rLS018+fPN6mpqYHj0tPTw7q6r7kizURXnd7e4XCYKVOmmPXr1wcdX1FRYZYsWVLnvNu9e3ezd+/eRseqfRV09XtPqion89xzz9W5KvGbb74xl1xySZ3n/Ywzzmjyuah5fKwy0VW/ZufOnRt0/LBhwxp9jTf1tzAcXq+3znvA4XCYO++803z//fdBx+7cudM8+OCDgXOn0+msc8V/U6+Ze+65xyQmJpoJEyaY559/3uTn5wed96odOHDA5Obm1rka3bIss3Tp0gYfS/Vz9Oijjwa1O/PMMxt9TteuXVunP5/PZySZI4880tx9993mgw8+aLBs1Pfff29mzJhhevbsGfb5GgAAAIgH1uRYk2NNjjU51uS+CDounmtyn376aZ3Mam632zzxxBOmqKgo6NiCggJzyy231DlH9e/f33g8nkbHaYlMa6tWrQq8Jnr37h10fFOvofrUbN+jRw/jdrsDfwPuuece8+OPPwYdv23bNnPffffVqS5w0EEHNVnatS0+P83Vku+L3NxcI1Vl23z00UfNl19+acrKyuoc5/f7zZdffmluvvlm43Q6g8a47777Guz/o48+CjxHNdv07t270efzo48+qre/k046yfTq1ctMmTLFvPrqq3X+RlXbs2ePmTdvnsnIyAgaNyUlxfz888+NPicAWh+C6AAArUJjC3bGGHP11VcH3d9U4EO4C3a1Fw4SEhKaLAexadMm07dv36B2RxxxRKPp4xsqMXfCCSc0+Y/bU089FdTG5XLV+cc52uqb6xFHHNHk8/n666/XaVc7iKa22qm/LcsyzzzzTKNtdu7cWafUR3Jycp1Fz1AeV2pqqvn2228bbVdTfenNu3fvbj755JNG202bNi2ozeGHHx5Y4LzrrrsabbtmzZo6iyhNPa8ejyfsQJuNGzcGBcTYbLY6C9f1idWCXXO88847df7B/t3vftfg8ZMmTQo6NiMjw2zevLnJcT7//POgUio2m81s3LixwePffvvtOq+bP/3pT42OUVxcbI499th6X7etNYhOqirp8s477zTabuvWrXVKU8yaNavRNg2VEhkwYECdxbLaHnvssTrtmio7U/PYWAXRVWuovFBLuPvuu4PGTkxMbDBIrdoXX3xhevToUe/vo6nXzJdfftnk373aZs6cGTTGqaee2mSb+sr/hsvv95v33nsvrDY7duwwQ4cODRp72bJlYY8NAAAAxBprcqzJsSbHmhxrcvVr6TU5n88XuDC15vujqdfAkiVL6rw+r7766kbbtESQWE21y2w2R32/B5fL1eR6y4cffhgI2K2+XXbZZY22aYvPT3O09Pvi+++/Nxs2bAhrjitWrAh6r3fr1q3Ji7CNiXxN15iq1059QX4N8fl8dcqDT58+vVljA4gfgugAAK1CUwt233//vXG5XEHHvPXWWw32F86C3ffff1/nn8zHH388pHl/+umnda70euWVVxo8vr4Fu969e5uffvqpybH8fr855phjgtouXLgwpHk2V33/lK5bty6ktueff35Q2xkzZjR47P79++sE0tx2220hjVNYWFjnn+BHHnkkrMdls9nMihUrQhqvWn0Ldi+//HKT7UpKSkzXrl3rtD3nnHNCGvfKK68M67E211tvvRU0TihXc7aWBbvPP//cdOnSJajvG2+8scHjv/nmG2NZVuDYlJSUBq8qq8+iRYuCxrr22msbPHb06NFBx1588cUhjfHjjz/WudI+Vgt2xkQniG7OnDkhjfXnP/85qN3YsWMbPb6+IDqn01lvBrH61L66cvDgwY0eH+mCSzjt4xVEt3///qDFKKnpYMZq//rXv+pdNAt3cTFUtTMKbtq0qdHjoxFE11zr1q0LOrdMnjy5xcYGAAAAQsWaHGtyrMmxJseaXMNack1u/vz5QWPY7XazZs2akNo+8sgjddbKGrvYtC0GidW3/vTss8+G1Pb555+v89zWrrxQU1t8fpqjLbwvjDHmb3/7W9A4zz33XJNtah7f3CC65vB6vaZ///6BsQ899NAWGxtAdAQXAwcAoJU69NBDdcsttwTtu/POO2WMibjvZ599VhUVFYHtwYMH66abbgqp7XHHHafLL788aN9TTz0V1vjTpk3TQQcd1ORxlmXp0ksvDdq3evXqsMaK1LXXXqsjjzwypGMnTZoUtN3YXF9++WUVFRUFtpOTk/X73/8+pHH69++vadOmBe0L93eQnZ2t448/Pqw2tWVmZuqiiy5q8rikpCSdeeaZdfY/+OCDIY1zwQUXBG2vWbMmtAmG6cwzz1SfPn0C2x9++GFMxom2jRs36le/+pVKSkoC+y688MJGXxOPPvpo0Lnk/vvvD3rsTbnwwgs1dOjQwPaiRYvk9/vrHFdQUKClS5cGth0Oh/73f/83pDH69u2rO+64I+Q5xduwYcN0xRVXhHRsNM5r119/vYYMGRLSsQ8//LCcTmdge8OGDVq2bFnYY7YnL7/8svbs2RPYPuqoo3T99deH1Pacc87RmDFjYjW1Omq/rj766KMWGztcRx55pEaOHBnYbivnUQAAAKAm1uRYk2sIa3KsydXEmlzkZs2aFbR9zTXX6Nhjjw2p7W233aYBAwYEtn0+n5555pmozq+1ycjI0DXXXBPSsVdeeaWGDRsW2K6srNRzzz0Xq6m1CW3lfSFV/V2z2+2B7dZ8XkxMTNTFF18c2P7hhx+0efPm+E0IQNgIogMAtBl33nmnkpOTA9tffvml5s2bF3G/7733XtD2NddcI5st9D+RN954Y9D2ihUrVFZWFnL72gtbjcnMzAza3rJlS8hto2Hy5MkhHxvOXGv/DiZOnKguXbqEPNa1114b9E/Uli1bVFhYGHL7yy67LORjG3LJJZeEfGzNxR2pKmAlIyMjpLY1/9mXYvcasCxLaWlpge2WXhxujh9//FFnnnmmdu3aFdh32mmnad68eQ2+p40xeuONNwLbdrs9rNd5tV/+8peBnz0ej77++us6x7z33ntBC4Nnn322DjnkkJDHuOqqq4Je561ZOOe1gw46SKmpqYHtHTt26MCBA2GNd/XVV4d8bJ8+fTR27NigfbXPQR1NzQUzqeq1Fs7fweuuuy7aU2pQenp60HZrPzfVnO+WLVu0e/fuOM4GAAAAaB7W5FiTawhrctHHmlx42suanNfr1YoVK4L2hbPe4nA4dO211wbte//996Myt9bqqquukmVZIR1rWVad9cOOvh7YFt4X1bp06RIU9N7az4ttbf0SQDCC6AAAbYbb7dY999wTtO93v/td2MEWNfl8Pq1atSpoX7gZdUaOHKlu3boFtg8cOFCnz4b0799fvXv3Dnms2sfWzBwUaw6HQ8OHDw/5+HDm+sknnwRth/s7OOSQQ+pkofr4449Dbn/CCSeENV59amYbakqvXr2i1jac10BlZaXeffdd3X777TrzzDOVlpam5ORkOZ1OWZZV51Zz4aakpCSsheiWVlxcrLPPPltbt24N7Bs+fLheffVVderUqcF2a9euDVrgO+KII9SjR4+wx+/Xr1+dfmv79NNPg7ZPP/30sMY4+OCDQ77qPN6OO+64sI6P5NzmdrvDOjdJdZ/72ouUHU2kr81wj69t9+7devbZZ3X11VdrxIgR6tu3r7p27SqbzVbnvDRo0KCgtj///HNEY4fLGKMVK1bo7rvv1jnnnKMBAwaoZ8+eSkhIqPc8On/+/LjOFwAAAIgG1uRYk2sIa3KhYU2uLtbkgq1atUo+ny+w3aNHD2VlZYXVR+3378qVK4P6bG8iXb9atWpVUDbUjibe74vy8nItWbJEN998s0499VQddthhcrvdcjgc9Z4Xt2/fHmgbj/W1/Px8Pfzww7r44ot11FFHqXfv3urcuXO9c73hhhuC2rIeCLQtjnhPAACAcEydOlVPPPFE4Eq/zZs366mnntJtt93WrP62b98etAjhcDh01FFHhdWHZVk65phjtHz58sC+zZs36+STT26ybSglI2pKSkoK2i4tLa33uIKCAhUUFITUp8vl0kknndTkccnJyXI4Qv/oEOpcjTF1rtw85phjQh6n2rHHHqtvvvkmsB1qiuykpKSwfw/1qb2Q1pjOnTsHbYczfu22DT2vNRljNGfOHN1zzz1B/2yGy+PxhFVSoaV4vV6de+65ysvLC+wbMGCA3nzzTXXt2rXRtrUX1tavXx/yFYyNqS/b1HfffRe0ffTRR4fd7zHHHBP0OFurWJ3b6lP7KvJQ1L56PNTzZXtkjAnKEmC320O+Cr9aSkqKDj30UP3www9htdu1a5fuvvtuzZkzp9mLuh6Pp1ntmuPVV1/V//zP/2jTpk3N7qMl5wsAAABEE2tyrMk1hDW5hrEm1zDW5ILVzuDYnDkOHTpUDocjEBjm9Xr1008/6dBDD43KHFsTu90e9t+MI488ss7zs3379qAKGR1JvN4XPp9Pjz/+uP74xz82e52sJdfXvvnmG9122211KnmEg/VAoG0hiA4A0KZ06tRJf/jDH3T55ZcH9v3xj3/U1Vdfre7du4fdX3FxcdB2jx49wlqUqlZ7saaoqCikdi6XK+yxaqqZbrumf/zjH/p//+//hdRHv379QlrcitVc9+7dq8rKyqB94Sx+NdQm1N9Bc1439Ynk+UlMTGx224ae12o+n0+TJk3SwoULmz1GtdZ41WtFRYUuuuiioKuc+/Tpo3feeSekhdBYlVas7x/j2vt69uwZdr/NaRMPsTpf1Ccaz2PtvwUdyd69e+X3+wPb3bp1k9PpDLufnj17hhVEt379ep1xxhkRfYkgtdx56bbbbtNf/vKXiPtpjedRAAAAIBSsyQVjTa7hNqzJVWFNrnGsyQWrfU5sznvR6XSqe/fuQc9tUVFRuwyia876lcPhkNvtDsqAWFxc3GGD6OLxvti3b5/Gjh2rDz/8MOyxaiovL4+ofagWLlyoSZMmRZzRsTWewwE0jCA6AECbM2nSJD366KP6+uuvJVX9w/3QQw/pwQcfDLuvffv2BW3XvlIzVF26dGm0XzSs9nNlWVadKztD0dzfQXOCRdqS22+/vc5iXefOnXXyyScrKytLqamp6tWrlxITE+uUWLj99tsD77PWyBija6+9Vq+//npgX/fu3fX2228rLS0tpD5idRVYzaCkatE43zT3HNWeReN57Mjn7Gj9HQyn3b59+zRmzJg6AXSpqak65ZRTNGTIEB1yyCHq1q2bEhMTg75I++mnnzR58uRmzbG5HnvssToBdE6nUyeeeKJGjhypww47TH369AmcR2teOT9jxgy98847LTpfAAAAIFZYk2tfWJOLLdbkGseaXLBonhNrBtG113NiJOtXNYPo2uvzE4p4vC8mTZpUJ4Cue/fuGjVqlI499lgdeuih6tmzpzp16qSEhISg4yZPnqyffvop7Dk21+eff66JEyfWCTY/+uijdeKJJ2rAgAHq27evOnfurMTERNlstsAx77zzjmbMmNFicwUQXQTRAQDaHJvNpocffli//OUvA/sef/xx3XTTTTrkkEPC6qt2Wvn9+/c3a04lJSWN9ouG1X6ujDEqLS0Ne9GO30Fd69at01NPPRW079e//rXuv/9+ud3uJts3Z+G0JU2bNk0vvPBCYDsxMVH/+te/6pTqbEztx3jkkUfqiSeeiHhu6enpdfZF43zT3HNUexaN57Ejny+i9XcwnHZ/+tOf9OOPPwa2U1JS9Mwzzyg7O7vJ0i0bN25s1vyaa/fu3br//vuD9l1yySV67LHHdPDBBzfZfvbs2bGaGgAAANDiWJNrX1iTix3W5JrGmlwwzonhidb6VXt9fkLR0u+Lt99+W//6178C23a7XX/4wx906623hnTOi0a553DceuutQQF0Rx99tObMmRNS2fPapXIBtC0E0QEA2qSzzz5bp59+ut577z1Jktfr1f3336/nnnsurH569OgRtF1cXKyKioqwy0f8/PPPQdvJyclhtY+26dOna/r06XGdQ6i6desmu90e9A/Jzz//rH79+oXVT2v7HbQGCxYsCLr68oorrtCf//znkNvHqqxCNDz66KN69NFHA9t2u10LFizQL37xi7D6qV0awRij0aNHR2WOtdVeJK151WOomtOmvYvG81j7b0G0lJaWxqTfaOrWrZtsNlvgXLF37175fL6wMwKE83t46aWXgrYXLVqkU089NaS2LX1eevXVV4MWoE877TS99NJLIS/ctebzKAAAANAcrMk1jjW5+P8OWgPW5JrGmlyw2ufE2u+rUPh8Pu3ZsydoX0Pvx0gDkuK95tWc9auKioo6GRAbWhNs689PKFr6fTFv3ryg7XvvvVd33XVXyO1DLRUeDQUFBfr0008D2z179lRubm7IZZZb8zkcQNNsTR8CAEDr9PDDDwf9MzNnzhytW7curD4OPvhgJSYmBrYrKiq0du3asPowxuirr74K2te/f/+w+ujILMuqszj35Zdfht3PF198EbTN70D6+OOPg7ZvvfXWkNvu379fhYWF0Z5SVPzjH//QtGnTgvY9/fTTOv/888Puq/bVqVu2bNGBAwciml9DDj/88KDt2ueNUDTnvdHeffPNN2G3qf3c13eVcjWXyxX4OdwFsJ07d4Y3sTiwLCuo1EplZaXy8vLC6mP37t364YcfQjr2+++/19atWwPbw4YNCzmATmre7zsStc+jN998c1gLqeE+lwAAAEBbwJpc+8CaXOywJtc01uSC1S6D25w5fvPNN6qoqAhsu1wu9e7du95ja653SW1vzas561fr1q2r8/w0VGWgrT8/oWjp90XN86LNZtPNN98cctuNGzfG7PxQn9rn8EsvvTTkADqp5dcvAUQXQXQAgDYrKytLF110UWC7srIyrCtXJMnpdCorKytoX25ublh9fP7550FXeCUkJNTpE4078cQTg7bD/R1s27atzmLtSSedFPG82rodO3YEbQ8ePDjktsuWLQtaVGgt3njjDV1zzTUyxgT2Pfjgg7rmmmua1d+IESPUrVu3wLbX663zT3K0HH/88UHbS5cuDav99u3btX79+mhOqV3weDxavXp1WG3ef//9oO0TTjihwWO7d+8e+Lm4uDisBZvPPvssrHnZbMH/ntV8ncdSpK/NcI6P5LwkSe+++25Yx0f6nEYy37Vr12r79u1hjQcAAAC0BazJtR+sycUGa3JNY00uWFZWVlBWteLiYq1atSqsPmq/f0eMGNFgpraa612S9NNPP4U8zo4dO7Rly5aw5haLNa/qjKihqr0emJWV1WD20/bw/DSlpd8XNc+LPXv2VEpKSshtw10PlIKzCbbkemBFRYWWLVsW1ngAWheC6AAAbdof//jHoH8EX3vtNX300Udh9XH66acHbc+ePTso3X5Tnn766aDtE088MehKWjSt9u/gpZdeCiqf15TnnnsuqPRE//7961y91xHV/ufQ5/OF3Pbxxx+P9nQi9sknn2jChAlBC4m//vWvdeeddza7T4fDobPOOitoXzjlNcJx2mmnBf3z/vbbb+vHH38Muf3f//73oNc5/uv5558P+dgdO3bojTfeCNpX+xxUU80r6CsqKvT111+HPNbcuXNDPlaSkpKSgrZbqvRD7cf/97//Pay/g+GUbYrkvLR161YtWbIk5OOlyJ/T9nYeBQAAAKKFNbn2gTW52Ghv/0uyJhf7NTmXy1XnIs9nnnkm5PaVlZWaPXt20L5Q17skhXWBau2ynKGIxZrXnDlzQg6OMsbUWT9s789PU1r6fVHzdxXOOdHv9+vJJ58M+fhqNZ/TllwPfOWVV+oE4QFoWwiiAwC0aYcffrhuuOGGoH21U8o35brrrgu64mjDhg0hfyhfuXKlXnjhhaB9OTk5YY0P6eKLL1ZycnJgu7i4WPfdd19Ibbdu3apHHnkkaB+/gyq109F/8MEHIbV76aWXwr7yONbWrl2rc845R16vN7Bv0qRJeuyxxyLuu/aC37/+9S/Nnz8/4n5rO/zww4MWZyoqKnT77beH1Hbbtm16+OGHoz6n9uLZZ58NuXTQHXfcEbTwMWjQIJ1yyikNHl87i0Gor43333+/TrBeU7p37y673R7YbqnyLRdffHHQ1d/r1q0LeaH29ddfD+t8Ufu89Mknn4S0EGWM0fXXXx/21fi1r2otKCgIq31zz6MffvhhWMGdAAAAQFvDmlz7wJpcbLAmFxrW5IJNmTIlaPv555+vUy65IY8//rg2bdoU2HY6nbruuusaPL72eteCBQtCGuenn37SjBkzQjq2pkjXZ+qTl5cX8oWdc+bMCbow1m63N5pFsT08P01p6fdFzfNicXFxyCVPH3roIW3YsCGssaTg53TXrl3at29fyG2bew7ftWuXfvvb34Y8DoDWiSA6AECbd99996lr166B7RUrVoR1Bcyhhx6qSZMmBe37n//5H7311luNtisoKFB2dnbQFbJHHHGELrjggpDHRpXOnTvr5ptvDtr3l7/8pc7Vc7Xt3r1b55xzjvbv3x/Yl5KS0ugCQUdy8sknB23fe++9TV5N/Oabb7a652/r1q0666yzVFxcHNh39tln6+9//3vQ1XLNNXz4cE2cODFo39VXX605c+aE1Y/X69XTTz+t//3f/23wmNr/RL/88st66KGHGu3X4/Ho3HPP1d69e8OaT0fi8/l0/vnnN1k68y9/+Yv+8Y9/BO377W9/2+jr6Nxzzw3afuqpp5rMRvfFF1/okksuCbtUgMPh0JFHHhnY3r17d9ilKZojKSmpzhcdt912W50yF7V99dVXuvzyy8MaKzU1Vf369Qts//TTT3rwwQcbbVNZWanrr79eb7/9dlhjSVK/fv2CAgS//PLLoEXlptQ+jz700ENNXk26evVqXXjhhWSOBAAAQLvHmlzbx5pcbLAmFxrW5IKNHz9eAwYMCGxXVlZq3Lhx2rx5c6PtXn/99ToBiZdffrn69u3bYJszzjhDLpcrsL169eomL6jcvXu3srOzwyptWm3o0KFB2wsXLgy7j/r8+te/1vLlyxs95uOPP9ZNN90UtO+SSy5Rampqg23ay/PTlJZ8X9Q+L95+++1NXiz7/PPPhxzYXVskz2ntuS5ZsqTJEq27du3Sr371qybXpwG0fgTRAQDavF69eul//ud/IurjL3/5iw477LDA9oEDB3TOOefoN7/5TZ1/Uj0ej5588kllZmYGpbd2Op2aN29eUBYhhO7uu+9WZmZmYNsYo2uvvVZXXHFFnQxTpaWlevHFFzVs2LA6VyzNnj1bbre7Jabc6l1++eVBV3R//fXXOvnkk/Xee+/VCe5Zt26dbrjhBp1zzjkqLS2Vy+Wqk7Y+XnJycoLea7169dKUKVP0wQcf6N133w3rVnPRr6Znnnkm6B/rsrIyXXXVVTrttNO0ePHiehcFjDEqLCzUyy+/rEmTJunggw/WjTfe2GiAztlnn13nC4K77rpLF154ob766qug/WVlZVqwYIGOPvporVmzRpIoiVKP6tdpfn6+jjnmGD3//PNBi/hS1VXTl156qW677bag/aeddlqjV51K0pgxYzRw4MDAdnl5uU4//XS9+OKLKi8vDzp269at+t3vfqeTTz5ZO3fuDFr4DNXZZ58dtJ2dna3f/va3WrBggd5+++2g13M4pSSact999wU9zrKyMp111lm6++6765Ry+Pnnn/Xwww/rxBNPVHFxsZxOpw499NCQx7rqqquCtu+//35NmTJFW7duDdp/4MABvfHGGxoxYkTgyuIhQ4aE9bgsy9KZZ54Z2Pb7/Ro1apTuu+8+LVq0SLm5uUHPae2/N9nZ2UF/U7Zt26YTTjhBixcvrrPQt3nzZt1111066aST9PP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p0ebNm7Vq1Sq9++67KisrU3V1tQoLC3XKKaf416xbt04TJkyQz+cL6oxZs2YpOTnZ/0hNTQ3+zQEAAAAAAERBWD+NN01Tzz33nAYPHqz77rsv4G7Enj176n/+53/0+eef69Zbb5UlyA+QAQDhZXnrrZDWrxs+XPXffCGwbt25cruP/hLBapVmzDgS0jkAAADhlpgYWj/WpKTQ1jud0qRJNpmm5PO1nZDn8xkyzcb5J1Jr1/T0dOXn52v06NFHVXRyOBwaPXq0CgoKIp5AZ5qm6urqdPDgQdXV1ckMZ6/e5hwOeRYtkgyj3UQ602KRDEOexYulIKtdAYgsj8cTMI6JiQlqXct5LfcJtwMHDujVV1/1j6+66ip95zvfCXr9k08+qfnz5ys3N1dxcXFHvd6jRw9NnTpV69ev19ChQ/3X33//fb344otBnXHffffp4MGD/scXIVbIBwAAAAAACLfgPvkJwuuvv6777rtPmzdvlmma/hYtsbGxKigo0G9+8xv1oiQRAHQt05T1hRdkqvUGrG0u/eZ/V4wd27SVVqwY2+rMl17y8L0fAADodlJSpPR0nyorj64A1xbDMJWWZqp379DOLyqyqq5OQZ/t8xmqqzO1cKFVBQXe0A7vRuLj45WTk6Ps7Gy53W41NDQoNjZW8fHxHWr32hlut1tlZWUqLS2Vs1l2osPhUHZ2trKyssLeQtaXmyvPsmWyTZoks65OkmQ0S9ozm96z3S7P4sXyjRkT1vMBdF5iYmLA2O12KyEhod11bre7zX3CraioSPX19f5xZ1u5tueUU07Rq6++qrPPPlsNDQ2SpN///veaMmVKu2vj4uICEvS4wRoAAAAAAHQ3IX9a8dFHH+myyy7ThAkTtHnzZv91wzB044036t///rcee+wxEugAoDvYv1+WysoOJ9BJjUl37VehM3XllT7l5QXXzgUAACCaDEOaOrVzyWj5+V6Fkt9lmtK8edZOrS0stCpShdK6kmEYstvtSk5Olt1uj3gCXUVFhQoLC7Vy5cqABDpJcjqdWrlypQoLC1VRURH2s325uaovL9eROXNktqiyZ6al6cicOarfti06CXSmKe3bJ2PHDmnfPp2Qf7iAMElKSgoY132TCNuelvN69OgRtpha89xzz/mf9+/fX+PGjYvYWRkZGbruuuv847KyMlVVVUXsPAAAAAAAgGjpdBLdli1blJeXp0suuUT//Oc/ZZqm//GDH/xAn376qZ5//nmlpqaGM14AQAiM2tqQ1q/+7nclHbsKncUiPfdcZNvUAAAAhGLyZK8SEiSLJbjEIYvFVEKCdMMNoVWC279fqqiwdKgCntRYta6iwqIDB0I6/qRXUVGhpUuXtttS0ePxaOnSpRFJpJPDIW9BgRrKyuSuqlL9li1yV1WpoaxM3oICKTk5/Gc253TKOneuYrOyFJ+aqrjMTMWnpio2K0vWuXN1QvUNBsKkb9++AeM9e/YEta7lvD59+oQtppZKS0u1fv16/3jKlCmyWjuXtB2syy+/PGC8ZcuWiJ4HAAAAAAAQDR1Ootu9e7duu+02nXPOOXrttdf8iXOSdOGFF+q9997TG2+8oaysrLAHCwAIjRliC5nDPXtKOnYVuqIi2rgCAIDuzeGQFi3yyDDaT6SzWEwZhrR4ceg/49TWhlZlraYmslXaTmRut1vFxcX+zy7aY5qmiouLj2rHGDaGIaWkyBw0qLHHcIQr8EmSpaREcRkZipk2TUZlZWA4lZWKmTZNcRkZspSURDwW4HiSmZkZMN6xY0dQ61rOGzJkSNhiaunZZ5/1PzcMQ7fcckvEzmpy2mmnBYz37dsX8TMBAAAAAAAirUNJdPfdd5/OPPNMPffcczpy5Ij/A+gzzzxTS5Ys8bd2BQB0U1arTMNQRxs2+SQd6NVLLrv9GFXoaOMKAACOH7m5Pi1b5pHdLhmGKcMI/Omo6ZrdLhUXezRmTOg/4yQmhtYyMymJlpudVVZW1m4FupY8Ho/KysoiFFF0WUpKZMvLk1wuGaYpo0Uyof+ayyVbXh6JdEAzZ599dsB47dq1Qa1rOW/o0KFhi6k5t9utRYsW+cejRo1Senp6RM5qrmW7Wru95U12AAAAAAAAx58OJdHNnj1bLpdLpmnKMAyddtppmj9/vjZt2qQf/ehHkYoRABAm1oULJdNUR2tdGJJWX3ihZBitVqGjjSsAADje5Ob6VF5erzlzjigtLTCpKC3N1Jw5R7RtW31YEuikxoJj6em+oxL22mMYptLTferdOyxhnHRM01RpaWmn1paWlgZdva7bcjplmzSp8XcAX9t/lg2fTzLNxvm0dgUkSampqTrjjDP841WrVgW1rvm8jIwMDRgwIOyxSdJf//pXOZv9/+utt94akXNaaplkfOqpp0blXAAAAAAAgEiK6cwi45tWI7W1tXrggQf0wAMPhC0gwzC0d+/esO0HAPiGaco6b17Hl33zv+vPOfeYVeho4woAAI5HDodUUOBVfr5XBw40tkxNSjLVu3f4O2wahjR1qlfTpnX81/D8fG80On6ekFwuV0CCSUc4nU653e7jusKStahIqqs7qvrcsRg+n8y6OlkXLpS3oCDC0QHHh2uuuUZz5syRJL333nvauXOnBg4ceMz5O3fuDEiiu+aaayIWW/NWrg6HIyo3OXu93oDqd4mJiRoxYkTEzwUAAAAAAIi0TiXRNTl8+LAkhfXObINvBgAgMvbvl6WiosPLmv5WNkxTa9bktKhCRxtXAABw/DOMxkpxKSmRrTo2ebJX06fHyOUy5fO1/7uvxdLYUvaGG7wRjetE1tE2ri01NDQcv0l0nbyJRpKshYXy5ueHP5sUOA7dfPPNevzxx+X1euXz+TRz5kw988wzx5w/Y8YM+b6p/Gi1WnXzzTdHJK7Kykq99957/vFPfvITxcfHR+Ss5ubMmaPNmzf7x+PHj1dsbGzEzwUAAAAAAIi0DrVzlRoT5lo+AADdn1FbG9L6uIZ6rVx5ecA12rgCAAAEz+GQFi3yyDAaE+TaYrGYMgxp8WIq/obCZrOFtP64Tgz55iaaYKvQNTFMs/HmmwMHwh+TaUr79snYsUPat69xDHRzQ4YM0U033eQfL1iwQAsWLGh17p/+9KeA6nBTpkxRZmZmq3O3b98uwzD8jylTpnQorueeey7gc9nOtnK94447VFRUpPr6+jbneTwe/fa3v9X999/vv2az2TRjxoxOnQsAAAAAANDddKgS3cMPPxypOAAAEWYmJoa0viEuTobR/Esu2rgCAAB0VG6uT8uWeTRpkk11dY0/W5nmt9W+mn7estsbE+jGjKHibyjsdrscDkenWro6HI6oVHWKlFBvojFqamSmpIQnGKdT1qIiWefNC6iO7UtPl3fqVHknTxa/WKA7mz17tlatWqVt27ZJkm677Ta9/vrruv7669WvXz/t2rVLixYt0htvvOFfk5GRod/97ncRicfn8+n555/3j4cPH67zzjuvU3uVlZXpqaeeUn5+vsaNG6fs7GydddZZcjgcslgs+uqrr/Svf/1LixYtUlVVlX+dYRhasGCBMjIyQn07AAAAAAAA3QJJdABwskhJkfeUU2TZu1cdacrkk+Ts1Usuu11xcQ1yuRIkSaNH08YVAACgM3JzfSovr9fChVYVFlpVUfHtT2dpaaby872aPNmr5OTIx2Ka0v79Um2tocREUykpJ1YHT8MwlJ2drZUrV3Z4bXZ2tozj+D9GqDfRmElJYYnDUlIi26RJUl3dUa8ZlZWKmTZNMdOny7NokXy5uWE5Ewi3Pn36aPny5Ro7dqwqKyslSa+99ppee+21VuenpaVp+fLl6tOnT0TiWbFiRUBCW2er0DV3+PBhLVmyREuWLGl3rsPh0Pz583XdddeFfC4AAAAAAEB30eF2rgCA49TBg7J0oiWTIWn1hRdKhqH6+qZ2VqZeeok2rgAAAJ3lcEgFBV6VlTWoqsqtLVvqVVXlVllZgwoKIp9A53RKc+dalZUVq9TUeGVmxik1NV5ZWbGaO9eqThRu67aysrI61NbVMAzZbDZlZWVFMKooSEmRLz1dZgcTAU3DkC89XerdO+QQLCUlsuXlSS6XDNM8qrWs/5rLJVteniwlJSGfCUTK4MGDtWHDBt15553q2bNnq3OSk5N15513asOGDRGt0Na8ZWx8fLxuuOGGTu81ceJEXXjhhUH9PXnqqafqvvvu06ZNm0igAwAAAAAAJ5wOVaI7lrq6OiUkJIRjK61YsUJXXHFFWPYCAHzLWlQkeTwdqkJnSvJarfr0nHN14EAvuVx2SdL11/vUq1dEwgQAADipGIaUkiKlpJjtTw6TkhLLN+1kj36tstLQtGkxmj49RosWeZSbe/xXHo6Pj9eECRO0dOlSmWZw/53z8vKO61aukiTDkHfqVMVMm9bhpd78/NBLEjqdjRXoTFOGr+0/R4bPJ9NikW3SJNWXl9PaFd1WUlKS/vCHP/jbu27fvl379+9XSkqKTj/9dI0aNUpxcXFB7XX66acH/XdSS8FUiwvWHXfcoTvuuEMNDQ3auHGjtm3bpj179qimpkY+n0/Jycnq27evzjvvPA0ePDhs5wIAAAAAAHQ3YUmimzhxot544w1ZLKEVtnvnnXeUl5en2tracIQFAGhimrLOm9eppXV2u+rj47V61YXSNyl4Dz5IFToAAIDjUUmJRXl5NpmmZJpHJ0k1XXO5TOXl2bRs2YmRSJeenq6JEyequLhYHs+xf5a12WzKy8tTWlpaFKOLHO/kyYqZPl2my9VuIpskmRaLZLfLG0JVqybWoiKpru6o6nPHYvh8MuvqZF24UN6CgpDPByIpPj5eY8eO7eowwio2NlYjRozQiBEjujoUAAAAAACALhGWdq5vv/22fvazn4W0xz/+8Q+NHz9ebrc7HCEBAJrbv1+WiooOVaGTGlPmetbUyO5yaf36cySZOvVUn06Q7xQBAABOKk6nNGlSYwKdz9f2T4Y+nyHTbJx/orR2TU9PV35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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_data_scaled[:,0,:])"
      ],
      "metadata": {
        "id": "vPSdFb7k006q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Divide datasets into train and test \n",
        "\n",
        "seed = 1000 # set a random seed to replicate results\n",
        "np.random.seed(seed)\n",
        "\n",
        "testvalset_ratio = 0.2 # ratio of test set points from the dataset\n",
        "\n",
        "X_train_scaled, X_test_scaled, Y_train_scaled, Y_test_scaled = train_test_split(X_data_scaled, Y_data_scaled[:,0,:],\n",
        "                                                    test_size=testvalset_ratio,random_state=seed\n",
        "                                                    )\n",
        "\n",
        "print(Y_train_scaled.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYGH5tjAGY7o",
        "outputId": "1b59d672-3565-4285-c915-2af0ef2fd05e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(16, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plots with normalized and Non-normalized outputs\n",
        "fig_ANN, (ax1_ANN, ax2_ANN) = plt.subplots(1,2)\n",
        "\n",
        "cases = np.arange(1, n_cases+1, 1)\n",
        "\n",
        "ax1_ANN.plot(X_test_scaled[:,:,0], X_test_scaled[:,:,1], 'o', markersize=10, color='black', label=\"testing points\") # show normalized training data\n",
        "ax1_ANN.plot(X_train_scaled[:,:,0], X_train_scaled[:,:,1], 'o', markersize=10, color='gray', label=\"training points\") # show normalized training data\n",
        "ax1_ANN.set_ylabel('Punch Force, $F_p$')\n",
        "ax1_ANN.set_xlabel('Punch Displacement, $y_p$')\n",
        "ax1_ANN.set_title('Normalized input data')\n",
        "# ax1_ANN.legend(['training', 'testing', 'validation'], loc='upper right')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "# ax1_ANN.set_ylim([-1.25, 1.25])\n",
        "# ax1_ANN.set_xlim([-1.25, 1.25])\n",
        "# Create figure with specified size\n",
        "fig_ANN.set_size_inches(22, 10)\n",
        "\n",
        "# K-n Points\n",
        "ax2_ANN.plot(cases, Y_data[:,0,:], 'o', markersize=10, color='gray') # show normalized training data\n",
        "# ax2_ANN.plot(cases, Y_test_scaled, 'o', markersize=10, color='black', label=\"testing points\") # show normalized training data\n",
        "ax2_ANN.set_ylabel('$K$')\n",
        "ax2_ANN.set_xlabel('$n$')\n",
        "ax2_ANN.set_title('Normalized output data')\n",
        "# ax2_ANN.legend(['training', 'testing', 'validation'], loc='upper right')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "# ax2_ANN.set_ylim([-1.25, 1.25])\n",
        "# ax2_ANN.set_xlim([-1.25, 1.25])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "BPd99iqt94r0",
        "outputId": "6527badb-4812-4e97-e65a-df2f80f6767c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Normalized output data')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=(500, 2), activation='tanh'))\n",
        "model.add(Dense(100, activation = \"tanh\"))\n",
        "model.add(Dense(1, activation = \"linear\"))"
      ],
      "metadata": {
        "id": "hpQ0q2bCEBCd"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "opt = tf.keras.optimizers.Adam(learning_rate=0.01)\n",
        "model.compile(loss='mean_squared_error',\n",
        "              optimizer=opt)\n",
        "model.summary()\n"
      ],
      "metadata": {
        "id": "d5C6_xALzyDG",
        "outputId": "5d6b466d-b2a3-46f7-b88d-51ec65f0f73e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 50)                10600     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               5100      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 15,801\n",
            "Trainable params: 15,801\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(X_train_scaled, Y_train_scaled, validation_data=(X_test_scaled, Y_test_scaled), epochs=500,  batch_size = 500)"
      ],
      "metadata": {
        "id": "wRykahqR0wTx",
        "outputId": "80c7d414-d0df-47e2-c244-82cdad51c182",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "1/1 [==============================] - 9s 9s/step - loss: 0.3234 - val_loss: 0.0555\n",
            "Epoch 2/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 1.2402 - val_loss: 0.4832\n",
            "Epoch 3/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2914 - val_loss: 1.1554\n",
            "Epoch 4/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.4612 - val_loss: 1.0965\n",
            "Epoch 5/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.4271 - val_loss: 0.8247\n",
            "Epoch 6/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.3243 - val_loss: 0.5859\n",
            "Epoch 7/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.3004 - val_loss: 0.4342\n",
            "Epoch 8/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.3275 - val_loss: 0.3777\n",
            "Epoch 9/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.3405 - val_loss: 0.3885\n",
            "Epoch 10/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.3242 - val_loss: 0.4380\n",
            "Epoch 11/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.2995 - val_loss: 0.5031\n",
            "Epoch 12/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2824 - val_loss: 0.5606\n",
            "Epoch 13/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.2757 - val_loss: 0.5815\n",
            "Epoch 14/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.2720 - val_loss: 0.5313\n",
            "Epoch 15/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.2582 - val_loss: 0.3736\n",
            "Epoch 16/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.2218 - val_loss: 0.0926\n",
            "Epoch 17/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.1647 - val_loss: 0.2149\n",
            "Epoch 18/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.3449 - val_loss: 0.1863\n",
            "Epoch 19/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.1524 - val_loss: 0.5162\n",
            "Epoch 20/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.2913 - val_loss: 0.6114\n",
            "Epoch 21/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.3234 - val_loss: 0.5412\n",
            "Epoch 22/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.2743 - val_loss: 0.4192\n",
            "Epoch 23/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2240 - val_loss: 0.3042\n",
            "Epoch 24/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2043 - val_loss: 0.2126\n",
            "Epoch 25/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.2096 - val_loss: 0.1456\n",
            "Epoch 26/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.2255 - val_loss: 0.1013\n",
            "Epoch 27/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.2371 - val_loss: 0.0766\n",
            "Epoch 28/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2309 - val_loss: 0.0672\n",
            "Epoch 29/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1995 - val_loss: 0.0683\n",
            "Epoch 30/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.1472 - val_loss: 0.0724\n",
            "Epoch 31/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0949 - val_loss: 0.0606\n",
            "Epoch 32/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0768 - val_loss: 0.0293\n",
            "Epoch 33/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0674 - val_loss: 0.2798\n",
            "Epoch 34/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0894 - val_loss: 0.1710\n",
            "Epoch 35/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0453 - val_loss: 0.0392\n",
            "Epoch 36/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0300 - val_loss: 0.0239\n",
            "Epoch 37/500\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 0.0342 - val_loss: 0.0219\n",
            "Epoch 38/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0352 - val_loss: 0.0198\n",
            "Epoch 39/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0453 - val_loss: 0.0191\n",
            "Epoch 40/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0497 - val_loss: 0.0196\n",
            "Epoch 41/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0402 - val_loss: 0.0209\n",
            "Epoch 42/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0290 - val_loss: 0.0231\n",
            "Epoch 43/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0282 - val_loss: 0.0333\n",
            "Epoch 44/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0239 - val_loss: 0.0675\n",
            "Epoch 45/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0224 - val_loss: 0.0985\n",
            "Epoch 46/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0340 - val_loss: 0.0794\n",
            "Epoch 47/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0266 - val_loss: 0.0519\n",
            "Epoch 48/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0291 - val_loss: 0.0401\n",
            "Epoch 49/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0199 - val_loss: 0.0329\n",
            "Epoch 50/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0204 - val_loss: 0.0246\n",
            "Epoch 51/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0231 - val_loss: 0.0196\n",
            "Epoch 52/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0222 - val_loss: 0.0195\n",
            "Epoch 53/500\n",
            "1/1 [==============================] - 0s 185ms/step - loss: 0.0245 - val_loss: 0.0196\n",
            "Epoch 54/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0248 - val_loss: 0.0191\n",
            "Epoch 55/500\n",
            "1/1 [==============================] - 0s 96ms/step - loss: 0.0210 - val_loss: 0.0218\n",
            "Epoch 56/500\n",
            "1/1 [==============================] - 0s 107ms/step - loss: 0.0201 - val_loss: 0.0265\n",
            "Epoch 57/500\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.0195 - val_loss: 0.0291\n",
            "Epoch 58/500\n",
            "1/1 [==============================] - 0s 116ms/step - loss: 0.0181 - val_loss: 0.0311\n",
            "Epoch 59/500\n",
            "1/1 [==============================] - 0s 91ms/step - loss: 0.0213 - val_loss: 0.0353\n",
            "Epoch 60/500\n",
            "1/1 [==============================] - 0s 94ms/step - loss: 0.0199 - val_loss: 0.0372\n",
            "Epoch 61/500\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.0207 - val_loss: 0.0311\n",
            "Epoch 62/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0189 - val_loss: 0.0237\n",
            "Epoch 63/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0182 - val_loss: 0.0205\n",
            "Epoch 64/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0189 - val_loss: 0.0196\n",
            "Epoch 65/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0183 - val_loss: 0.0193\n",
            "Epoch 66/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0194 - val_loss: 0.0191\n",
            "Epoch 67/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0192 - val_loss: 0.0190\n",
            "Epoch 68/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0185 - val_loss: 0.0195\n",
            "Epoch 69/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0186 - val_loss: 0.0215\n",
            "Epoch 70/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0177 - val_loss: 0.0249\n",
            "Epoch 71/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0180 - val_loss: 0.0269\n",
            "Epoch 72/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0182 - val_loss: 0.0263\n",
            "Epoch 73/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0182 - val_loss: 0.0254\n",
            "Epoch 74/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0185 - val_loss: 0.0252\n",
            "Epoch 75/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0178 - val_loss: 0.0242\n",
            "Epoch 76/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0180 - val_loss: 0.0218\n",
            "Epoch 77/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0176 - val_loss: 0.0199\n",
            "Epoch 78/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0178 - val_loss: 0.0193\n",
            "Epoch 79/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0180 - val_loss: 0.0194\n",
            "Epoch 80/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0178 - val_loss: 0.0198\n",
            "Epoch 81/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0180 - val_loss: 0.0201\n",
            "Epoch 82/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0177 - val_loss: 0.0205\n",
            "Epoch 83/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0177 - val_loss: 0.0215\n",
            "Epoch 84/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0176 - val_loss: 0.0229\n",
            "Epoch 85/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0176 - val_loss: 0.0236\n",
            "Epoch 86/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0178 - val_loss: 0.0230\n",
            "Epoch 87/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0176 - val_loss: 0.0221\n",
            "Epoch 88/500\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0177 - val_loss: 0.0216\n",
            "Epoch 89/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0175 - val_loss: 0.0211\n",
            "Epoch 90/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0176 - val_loss: 0.0204\n",
            "Epoch 91/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0175 - val_loss: 0.0197\n",
            "Epoch 92/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0176 - val_loss: 0.0195\n",
            "Epoch 93/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0176 - val_loss: 0.0199\n",
            "Epoch 94/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0175 - val_loss: 0.0204\n",
            "Epoch 95/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0175 - val_loss: 0.0207\n",
            "Epoch 96/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0175 - val_loss: 0.0209\n",
            "Epoch 97/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0175 - val_loss: 0.0214\n",
            "Epoch 98/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0175 - val_loss: 0.0218\n",
            "Epoch 99/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0175 - val_loss: 0.0217\n",
            "Epoch 100/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0175 - val_loss: 0.0211\n",
            "Epoch 101/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0175 - val_loss: 0.0206\n",
            "Epoch 102/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0175 - val_loss: 0.0204\n",
            "Epoch 103/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0175 - val_loss: 0.0202\n",
            "Epoch 104/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0175 - val_loss: 0.0199\n",
            "Epoch 105/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0175 - val_loss: 0.0198\n",
            "Epoch 106/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0175 - val_loss: 0.0200\n",
            "Epoch 107/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0174 - val_loss: 0.0203\n",
            "Epoch 108/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0174 - val_loss: 0.0205\n",
            "Epoch 109/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0174 - val_loss: 0.0206\n",
            "Epoch 110/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0174 - val_loss: 0.0207\n",
            "Epoch 111/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0174 - val_loss: 0.0208\n",
            "Epoch 112/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0174 - val_loss: 0.0206\n",
            "Epoch 113/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0174 - val_loss: 0.0203\n",
            "Epoch 114/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0200\n",
            "Epoch 115/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0174 - val_loss: 0.0199\n",
            "Epoch 116/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0174 - val_loss: 0.0199\n",
            "Epoch 117/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0198\n",
            "Epoch 118/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0174 - val_loss: 0.0198\n",
            "Epoch 119/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0199\n",
            "Epoch 120/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0201\n",
            "Epoch 121/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0174 - val_loss: 0.0201\n",
            "Epoch 122/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0201\n",
            "Epoch 123/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0201\n",
            "Epoch 124/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0174 - val_loss: 0.0200\n",
            "Epoch 125/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0174 - val_loss: 0.0199\n",
            "Epoch 126/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0174 - val_loss: 0.0197\n",
            "Epoch 127/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 128/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 129/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 130/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 131/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 132/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0197\n",
            "Epoch 133/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0174 - val_loss: 0.0197\n",
            "Epoch 134/500\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0174 - val_loss: 0.0197\n",
            "Epoch 135/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 136/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0196\n",
            "Epoch 137/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0195\n",
            "Epoch 138/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0174 - val_loss: 0.0194\n",
            "Epoch 139/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0174 - val_loss: 0.0194\n",
            "Epoch 140/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0174 - val_loss: 0.0193\n",
            "Epoch 141/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0174 - val_loss: 0.0194\n",
            "Epoch 142/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0173 - val_loss: 0.0193\n",
            "Epoch 143/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0173 - val_loss: 0.0194\n",
            "Epoch 144/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0173 - val_loss: 0.0194\n",
            "Epoch 145/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0173 - val_loss: 0.0194\n",
            "Epoch 146/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0173 - val_loss: 0.0194\n",
            "Epoch 147/500\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0173 - val_loss: 0.0193\n",
            "Epoch 148/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0173 - val_loss: 0.0193\n",
            "Epoch 149/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0192\n",
            "Epoch 150/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0173 - val_loss: 0.0192\n",
            "Epoch 151/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 152/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 153/500\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 154/500\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 155/500\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 156/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 157/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 158/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0191\n",
            "Epoch 159/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0190\n",
            "Epoch 160/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0173 - val_loss: 0.0190\n",
            "Epoch 161/500\n",
            "1/1 [==============================] - 0s 82ms/step - loss: 0.0173 - val_loss: 0.0190\n",
            "Epoch 162/500\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0173 - val_loss: 0.0189\n",
            "Epoch 163/500\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.0173 - val_loss: 0.0189\n",
            "Epoch 164/500\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.0173 - val_loss: 0.0189\n",
            "Epoch 165/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0173 - val_loss: 0.0189\n",
            "Epoch 166/500\n",
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            "Epoch 169/500\n",
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            "Epoch 172/500\n",
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            "Epoch 173/500\n",
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            "Epoch 174/500\n",
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            "Epoch 175/500\n",
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            "Epoch 176/500\n",
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            "Epoch 177/500\n",
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            "Epoch 178/500\n",
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            "Epoch 179/500\n",
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            "Epoch 180/500\n",
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            "Epoch 181/500\n",
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            "Epoch 182/500\n",
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            "Epoch 183/500\n",
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            "Epoch 184/500\n",
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            "Epoch 185/500\n",
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            "Epoch 186/500\n",
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            "Epoch 187/500\n",
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            "Epoch 188/500\n",
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            "Epoch 189/500\n",
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            "Epoch 190/500\n",
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            "Epoch 191/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0173 - val_loss: 0.0186\n",
            "Epoch 192/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0173 - val_loss: 0.0185\n",
            "Epoch 193/500\n",
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            "Epoch 194/500\n",
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            "Epoch 195/500\n",
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            "Epoch 196/500\n",
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            "Epoch 197/500\n",
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            "Epoch 198/500\n",
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            "Epoch 199/500\n",
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            "Epoch 200/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0173 - val_loss: 0.0185\n",
            "Epoch 201/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0173 - val_loss: 0.0185\n",
            "Epoch 202/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0173 - val_loss: 0.0185\n",
            "Epoch 203/500\n",
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            "Epoch 204/500\n",
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            "Epoch 205/500\n",
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            "Epoch 206/500\n",
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            "Epoch 207/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0173 - val_loss: 0.0184\n",
            "Epoch 208/500\n",
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            "Epoch 209/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0173 - val_loss: 0.0184\n",
            "Epoch 210/500\n",
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            "Epoch 211/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0184\n",
            "Epoch 212/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0173 - val_loss: 0.0184\n",
            "Epoch 213/500\n",
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            "Epoch 214/500\n",
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            "Epoch 215/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0173 - val_loss: 0.0184\n",
            "Epoch 216/500\n",
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            "Epoch 217/500\n",
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            "Epoch 218/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 219/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 220/500\n",
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            "Epoch 221/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 222/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 223/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 224/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 225/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 226/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 227/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 228/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 229/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 230/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 231/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 232/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 233/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 234/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 235/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 236/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 237/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 238/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 239/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 240/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 241/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 242/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 243/500\n",
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            "Epoch 244/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 245/500\n",
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            "Epoch 246/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 247/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 248/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 249/500\n",
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            "Epoch 250/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 251/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 252/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 253/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 254/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 255/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 256/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 257/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 258/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 259/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 260/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 261/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 262/500\n",
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            "Epoch 263/500\n",
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            "Epoch 264/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 265/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 266/500\n",
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            "Epoch 267/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 268/500\n",
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            "Epoch 269/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 270/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 271/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 272/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 273/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 274/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 275/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 276/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 277/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 278/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 279/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 280/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 281/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 282/500\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 283/500\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 284/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 285/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 286/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 287/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 288/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 289/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 290/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 291/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 292/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 293/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 294/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 295/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 296/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 297/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 298/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 299/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 300/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 301/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 302/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 303/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 304/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 305/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 306/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 307/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 308/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 309/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 310/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 311/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 312/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 313/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 314/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 315/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 316/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 317/500\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 318/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 319/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 320/500\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 321/500\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 322/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 323/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 324/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 325/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 326/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 327/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 328/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 329/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 330/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 331/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 332/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 333/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 334/500\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 335/500\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0172 - val_loss: 0.0182\n",
            "Epoch 336/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 337/500\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 338/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 339/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 340/500\n",
            "1/1 [==============================] - 0s 90ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 341/500\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 342/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 343/500\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 344/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 345/500\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 346/500\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 347/500\n",
            "1/1 [==============================] - 0s 79ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 348/500\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 349/500\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 350/500\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 351/500\n",
            "1/1 [==============================] - 0s 82ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 352/500\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 353/500\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 354/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 355/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 356/500\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 357/500\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 358/500\n",
            "1/1 [==============================] - 0s 87ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 359/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 360/500\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 361/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 362/500\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 363/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 364/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 365/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 366/500\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 367/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 368/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 369/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 370/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 371/500\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 372/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 373/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 374/500\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 375/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 376/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 377/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 378/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 379/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 380/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 381/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 382/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 383/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 384/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 385/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 386/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 387/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 388/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 389/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 390/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 391/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 392/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 393/500\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 394/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 395/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 396/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 397/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 398/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 399/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 400/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 401/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 402/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 403/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 404/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 405/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 406/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 407/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 408/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 409/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0171 - val_loss: 0.0184\n",
            "Epoch 410/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0171 - val_loss: 0.0183\n",
            "Epoch 411/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0171 - val_loss: 0.0185\n",
            "Epoch 412/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0171 - val_loss: 0.0182\n",
            "Epoch 413/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0186\n",
            "Epoch 414/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0173 - val_loss: 0.0180\n",
            "Epoch 415/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0176 - val_loss: 0.0192\n",
            "Epoch 416/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0187 - val_loss: 0.0175\n",
            "Epoch 417/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0213 - val_loss: 0.0204\n",
            "Epoch 418/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0286 - val_loss: 0.0182\n",
            "Epoch 419/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0385 - val_loss: 0.0186\n",
            "Epoch 420/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0453 - val_loss: 0.0193\n",
            "Epoch 421/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0241 - val_loss: 0.0197\n",
            "Epoch 422/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0263 - val_loss: 0.0181\n",
            "Epoch 423/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0343 - val_loss: 0.0176\n",
            "Epoch 424/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0183 - val_loss: 0.0190\n",
            "Epoch 425/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0252 - val_loss: 0.0310\n",
            "Epoch 426/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0340 - val_loss: 0.0195\n",
            "Epoch 427/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0195 - val_loss: 0.0175\n",
            "Epoch 428/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0222 - val_loss: 0.0182\n",
            "Epoch 429/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0239 - val_loss: 0.0171\n",
            "Epoch 430/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0196 - val_loss: 0.0173\n",
            "Epoch 431/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0237 - val_loss: 0.0178\n",
            "Epoch 432/500\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0175 - val_loss: 0.0221\n",
            "Epoch 433/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0227 - val_loss: 0.0195\n",
            "Epoch 434/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0180 - val_loss: 0.0181\n",
            "Epoch 435/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0208 - val_loss: 0.0186\n",
            "Epoch 436/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0188 - val_loss: 0.0173\n",
            "Epoch 437/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0190 - val_loss: 0.0168\n",
            "Epoch 438/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0191 - val_loss: 0.0168\n",
            "Epoch 439/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0186 - val_loss: 0.0168\n",
            "Epoch 440/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0188 - val_loss: 0.0171\n",
            "Epoch 441/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0180 - val_loss: 0.0169\n",
            "Epoch 442/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0189 - val_loss: 0.0172\n",
            "Epoch 443/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0177 - val_loss: 0.0176\n",
            "Epoch 444/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0187 - val_loss: 0.0168\n",
            "Epoch 445/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0174 - val_loss: 0.0167\n",
            "Epoch 446/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0185 - val_loss: 0.0166\n",
            "Epoch 447/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0175 - val_loss: 0.0167\n",
            "Epoch 448/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0183 - val_loss: 0.0168\n",
            "Epoch 449/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0173 - val_loss: 0.0170\n",
            "Epoch 450/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0181 - val_loss: 0.0176\n",
            "Epoch 451/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0174 - val_loss: 0.0181\n",
            "Epoch 452/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0180 - val_loss: 0.0174\n",
            "Epoch 453/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0173 - val_loss: 0.0170\n",
            "Epoch 454/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0178 - val_loss: 0.0171\n",
            "Epoch 455/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0173\n",
            "Epoch 456/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0177 - val_loss: 0.0173\n",
            "Epoch 457/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0176\n",
            "Epoch 458/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0176 - val_loss: 0.0183\n",
            "Epoch 459/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0173 - val_loss: 0.0187\n",
            "Epoch 460/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0175 - val_loss: 0.0181\n",
            "Epoch 461/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0173 - val_loss: 0.0177\n",
            "Epoch 462/500\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0174 - val_loss: 0.0177\n",
            "Epoch 463/500\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0173 - val_loss: 0.0177\n",
            "Epoch 464/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0174 - val_loss: 0.0176\n",
            "Epoch 465/500\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0173 - val_loss: 0.0178\n",
            "Epoch 466/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0173 - val_loss: 0.0183\n",
            "Epoch 467/500\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.0173 - val_loss: 0.0185\n",
            "Epoch 468/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0173 - val_loss: 0.0182\n",
            "Epoch 469/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0173 - val_loss: 0.0180\n",
            "Epoch 470/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0181\n",
            "Epoch 471/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0173 - val_loss: 0.0179\n",
            "Epoch 472/500\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0172 - val_loss: 0.0178\n",
            "Epoch 473/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0173 - val_loss: 0.0180\n",
            "Epoch 474/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0184\n",
            "Epoch 475/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0172 - val_loss: 0.0185\n",
            "Epoch 476/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0184\n",
            "Epoch 477/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0185\n",
            "Epoch 478/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0185\n",
            "Epoch 479/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 480/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0172 - val_loss: 0.0183\n",
            "Epoch 481/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0185\n",
            "Epoch 482/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0187\n",
            "Epoch 483/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0172 - val_loss: 0.0187\n",
            "Epoch 484/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 485/500\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0172 - val_loss: 0.0189\n",
            "Epoch 486/500\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 487/500\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0172 - val_loss: 0.0186\n",
            "Epoch 488/500\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0172 - val_loss: 0.0186\n",
            "Epoch 489/500\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 490/500\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 491/500\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 492/500\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0172 - val_loss: 0.0189\n",
            "Epoch 493/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0189\n",
            "Epoch 494/500\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 495/500\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0172 - val_loss: 0.0187\n",
            "Epoch 496/500\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0172 - val_loss: 0.0187\n",
            "Epoch 497/500\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.0172 - val_loss: 0.0187\n",
            "Epoch 498/500\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0172 - val_loss: 0.0187\n",
            "Epoch 499/500\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0172 - val_loss: 0.0188\n",
            "Epoch 500/500\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0172 - val_loss: 0.0189\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_ANN, (ax1_ANN) = plt.subplots(1,1)\n",
        "\n",
        "ax1_ANN.plot(history.history['loss'])\n",
        "ax1_ANN.plot(history.history['val_loss'])\n",
        "ax1_ANN.set_title('Training and testing loss')\n",
        "ax1_ANN.set_ylabel('loss')\n",
        "ax1_ANN.set_xlabel('epoch')\n",
        "ax1_ANN.legend(['training', 'validation'], loc='upper right')\n",
        "#ax1_ANN.grid(True, color='gray', linestyle='-')\n",
        "ax1_ANN.set_xlim([0, 1000])\n",
        "ax1_ANN.set_ylim([0, 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "kyJMlWXjGKzV",
        "outputId": "2b63acc7-3c44-446c-fa8f-d56def38b941"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_data_ANNpred_scaled = history.model.predict(X_data_scaled)\n",
        "Y_data_ANNpred = scaler_y.inverse_transform(Y_data_ANNpred_scaled)\n",
        "Y_data_ANNpred_scaled = Y_data_ANNpred_scaled.reshape(n_cases,1,1)\n",
        "Y_data_ANNpred = Y_data_ANNpred.reshape(n_cases,1,1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAMxXRvKGqYV",
        "outputId": "d6525262-dc19-422f-bb06-a2decd0b3c07"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 357ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_ANN, (ax1_ANN, ax2_ANN) = plt.subplots(1,2)\n",
        "\n",
        "cases = np.arange(1, n_cases+1, 1)\n",
        "ax1_ANN.plot(cases, Y_data_scaled[:,0,:], 'o', markersize=10, color='black', label=\"testing points\") # show training data\n",
        "ax1_ANN.plot(cases, Y_data_ANNpred_scaled[:,0,:], marker='*', linestyle='none', color='red', label=\"Neural Network prediction\") # plot prediction\n",
        "ax1_ANN.set_ylabel('$K$')\n",
        "ax1_ANN.set_xlabel('$cases$')\n",
        "\n",
        "\n",
        "ax2_ANN.plot(cases, Y_data[:,0,:], 'o', markersize=10, color='black', label=\"testing points\") # show training data\n",
        "ax2_ANN.plot(cases, Y_data_ANNpred[:,0,:], marker='*', linestyle='none', color='red', label=\"Neural Network prediction\") # plot prediction\n",
        "ax2_ANN.set_ylabel('$K$')\n",
        "ax2_ANN.set_xlabel('$cases$')\n",
        "\n",
        "# ax2_ANN.plot(Y_data[:,1,:], Y_data[:,0,:], 'o', markersize=10, color='gray', label=\"training points\") # show training data\n",
        "# ax2_ANN.plot(Y_data_ANNpred[:,1], Y_data_ANNpred[:,0], marker='*', linestyle='none', color='red', label=\"Neural Network prediction\") # plot prediction\n",
        "# ax2_ANN.set_ylabel('$K$')\n",
        "# ax2_ANN.set_xlabel('$n$')\n",
        "# # ax2_ANN.legend(loc='best')\n",
        "# ax2_ANN.set_xlim([0, 0.4])\n",
        "# ax2_ANN.set_ylim([200, 1800])\n",
        "#ax2_ANN.grid(True, color='gray', linestyle='-')\n",
        "# Create figure with specified size\n",
        "fig_ANN.set_size_inches(22, 8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "e0cVdiS7Ga78",
        "outputId": "d467c21b-df3c-41cd-b771-4139ea996347"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x800 with 2 Axes>"
            ],
            "image/png": 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    }
  ]
}